Load data
Compute data-driven consensus
Cell types mapped
Mapping summaries
Here we break the links from cytotoxic cells to other cytotoxic cell types such as nk, ilc, tgd, and t cd8. These are useful for retaining higher level annotations but we want them to be an independent tree for the purposes of determining depth of annotation.
In a compositional analysis, we will be selecting a specific level of the immune tree hierarchy. Below we collapse the nodes of the tree to a specific level and assess how many cells remain annotated at each level. As we go for finer resolutions, we get fewer cells annotated. The plot below shows that even at the finest resolution (L3), we get ~10M cells annotated.
Resolving missing values at deeper annotation levels
Some cells will have missing annotations at specific levels. This is because neither the original annotation, nor our annotation were able to further resolve their type in the cell type hierarcy. This can happen in three possible scenarios:
- Annotation is missing using one approach and the other is only able to provide a high level annotation.
- The low level annotation is the best possible using both approaches (one might have a higher level annotation).
- Cell types called using both approaches end up being siblings therefore the parent annotation is the best we can achieve.
# A tibble: 31 × 3
cell_type_unified cell_type_unified_ensemble NCells
<chr> <chr> <dbl>
1 other other 2039871
2 macrophage other 72296
3 ilc other 21110
4 t other 19562
5 t cd4 other 15339
6 nk other 10242
7 cd8 tcm other 7332
8 plasma other 6629
9 mast other 6544
10 monocytic other 6314
11 treg other 5957
12 cd4 naive other 5573
13 dc other 5247
14 cdc other 3956
15 b other 2753
16 erythrocyte other 2155
17 cytotoxic other 1939
18 t cd8 other 1938
19 nkt other 1005
20 granulocyte other 870
21 pdc other 835
22 cd8 tem other 362
23 b naive other 199
24 cd4 tem other 107
25 cd14 mono other 93
26 b memory other 84
27 tgd other 27
28 cd4 tcm other 14
29 cd8 naive other 12
30 mait other 3
31 cd4 th1 em other 1Dataset summaries
Summary per dataset
Summary per organ
Session Info
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Australia/Sydney
tzcode source: internal
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggalluvial_0.12.5 scico_1.5.0 ggraph_2.2.1
[4] tidygraph_1.3.1 ComplexHeatmap_2.20.0 tidyHeatmap_1.10.2
[7] DT_0.33 CuratedAtlasQueryR_1.3.6 igraph_2.1.1
[10] BiocParallel_1.38.0 patchwork_1.3.0 arrow_17.0.0.1
[13] duckdb_1.1.1 DBI_1.2.3 lubridate_1.9.3
[16] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[19] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[22] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.4.0
[3] later_1.3.2 polyclip_1.10-7
[5] fastDummies_1.7.4 lifecycle_1.0.4
[7] doParallel_1.0.17 globals_0.16.3
[9] lattice_0.22-6 MASS_7.3-61
[11] dendextend_1.18.1 backports_1.5.0
[13] magrittr_2.0.3 plotly_4.10.4
[15] rmarkdown_2.28 yaml_2.3.10
[17] httpuv_1.6.15 Seurat_5.1.0
[19] sctransform_0.4.1 spam_2.11-0
[21] sp_2.1-4 spatstat.sparse_3.1-0
[23] reticulate_1.39.0 cowplot_1.1.3
[25] pbapply_1.7-2 RColorBrewer_1.1-3
[27] abind_1.4-8 zlibbioc_1.50.0
[29] Rtsne_0.17 GenomicRanges_1.56.2
[31] BiocGenerics_0.50.0 tweenr_2.0.3
[33] circlize_0.4.16 GenomeInfoDbData_1.2.12
[35] IRanges_2.38.1 S4Vectors_0.42.1
[37] ggrepel_0.9.6 irlba_2.3.5.1
[39] listenv_0.9.1 spatstat.utils_3.1-0
[41] goftest_1.2-3 RSpectra_0.16-2
[43] spatstat.random_3.3-2 fitdistrplus_1.2-1
[45] parallelly_1.38.0 leiden_0.4.3.1
[47] codetools_0.2-20 DelayedArray_0.30.1
[49] ggforce_0.4.2 shape_1.4.6.1
[51] tidyselect_1.2.1 UCSC.utils_1.0.0
[53] farver_2.1.2 viridis_0.6.5
[55] matrixStats_1.4.1 stats4_4.4.0
[57] spatstat.explore_3.3-3 jsonlite_1.8.9
[59] GetoptLong_1.0.5 progressr_0.14.0
[61] iterators_1.0.14 ggridges_0.5.6
[63] survival_3.7-0 foreach_1.5.2
[65] tools_4.4.0 ica_1.0-3
[67] Rcpp_1.0.13 glue_1.8.0
[69] gridExtra_2.3 SparseArray_1.4.8
[71] xfun_0.48 MatrixGenerics_1.16.0
[73] ggthemes_5.1.0 GenomeInfoDb_1.40.1
[75] HDF5Array_1.32.1 withr_3.0.1
[77] fastmap_1.2.0 rhdf5filters_1.16.0
[79] fansi_1.0.6 digest_0.6.37
[81] timechange_0.3.0 R6_2.5.1
[83] mime_0.12 colorspace_2.1-1
[85] Cairo_1.6-2 scattermore_1.2
[87] tensor_1.5 spatstat.data_3.1-2
[89] utf8_1.2.4 generics_0.1.3
[91] data.table_1.16.2 graphlayouts_1.2.0
[93] httr_1.4.7 htmlwidgets_1.6.4
[95] S4Arrays_1.4.1 uwot_0.2.2
[97] pkgconfig_2.0.3 gtable_0.3.5
[99] blob_1.2.4 lmtest_0.9-40
[101] SingleCellExperiment_1.26.0 XVector_0.44.0
[103] htmltools_0.5.8.1 vissE_1.12.0
[105] dotCall64_1.2 clue_0.3-65
[107] SeuratObject_5.0.2 scales_1.3.0
[109] Biobase_2.64.0 png_0.1-8
[111] spatstat.univar_3.0-1 knitr_1.48
[113] rjson_0.2.23 tzdb_0.4.0
[115] reshape2_1.4.4 checkmate_2.3.2
[117] nlme_3.1-166 cachem_1.1.0
[119] GlobalOptions_0.1.2 zoo_1.8-12
[121] rhdf5_2.48.0 KernSmooth_2.23-24
[123] parallel_4.4.0 miniUI_0.1.1.1
[125] pillar_1.9.0 vctrs_0.6.5
[127] RANN_2.6.2 promises_1.3.0
[129] dbplyr_2.5.0 xtable_1.8-4
[131] cluster_2.1.6 evaluate_1.0.1
[133] magick_2.8.5 cli_3.6.3
[135] compiler_4.4.0 rlang_1.1.4
[137] crayon_1.5.3 future.apply_1.11.2
[139] labeling_0.4.3 plyr_1.8.9
[141] stringi_1.8.4 viridisLite_0.4.2
[143] deldir_2.0-4 assertthat_0.2.1
[145] munsell_0.5.1 lazyeval_0.2.2
[147] spatstat.geom_3.3-3 Matrix_1.7-1
[149] RcppHNSW_0.6.0 hms_1.1.3
[151] bit64_4.5.2 future_1.34.0
[153] Rhdf5lib_1.26.0 shiny_1.9.1
[155] SummarizedExperiment_1.34.0 ROCR_1.0-11
[157] memoise_2.0.1 bit_4.5.0